Collaboration adaptive filtering model for data reduction in wireless sensor networks
by Walaa M. Elsayed; Hazem M. El-Bakry; Salah M. El-Sayed
International Journal of Hybrid Intelligence (IJHI), Vol. 1, No. 4, 2019

Abstract: Wireless sensor networks (WSNs) are collecting data periodically by randomly dispersed sensors (motes) that typically exploit high energy in monitoring a specified application. Furthermore, dissemination mode in WSN is resulting noisy or missing information that affects the behaviour of WSN. So, data prediction-based filtering is an important approach to reduce redundant data transmissions, conserve node energy and overcome the defects resulted from data dissemination. Therefore, in this article, a distributed data-reduction model (DDRM) is proposed to prolong the network lifetime by decreasing the energy consumption of sensor nodes. It is built upon a distributive clustering model for predicting diffusion-faults in WSN. The proposed model is developed using the RLS adaptive filter integrated with a FIR filter for minimising the amount of transmitted data and provide high convergence of the signals. A dataset of atmospheric changes was handled. The results clarify that DDRM reduced the rate of data transmission to ~20%. Also, it depressed the energy consumption to ~95% throughout the dataset sample. DDRM effectively upgraded the performance of the sensory network by about 19.5%, and hence extend its lifetime.

Online publication date: Mon, 20-Apr-2020

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